Wednesday, December 28, 2016

ICRON Technologies, a leading developer of Advanced Planning and Scheduling solutions since 1992, has been in Aerospace MRO (Maintenance, Repair and Overhaul) optimization for more than a decade and shares its experience in this area in this webinar below.

In this webinar, you will discover how a next generation MRO planning and scheduling solution can improve your MRO activities and KPIs such as TRT times, overtime and productivity.

See below for a detailed description of the topic :

Modern facilities are text book cases of complex production environments :

100% Make-to-order

Capital-intensive production processes

High product mix, large number of components or production tasks

frequent schedule changes

Planning and scheduling of thousands or tens of thousands of repair operations w.r.t tight physical resources, regulations, certifications, spare parts, etc. in this environment is a very difficult job. Frequent rescheduling due to post strip/inspection repair needs, unplanned Aircraft On Ground (AOG) repairs and part cannibalization, machine breakdowns, spare part unavailability's, certified personnel absence, etc. also makes planning more complex. More over, MRO plan, shop floor execution data and ERP data are not blended and visualized so the shop floor is a black box so the environment is pretty much a black box.

Current ERP systems also do not help a lot when it comes to planning and scheduling. Most ERPs in MRO are originally developed for mass production, old fashion manufacturing. They assume capacity problems are solved in advance and they plan as if the environment has infinite capacity.

One of ICRON's biggest feature is Finite Capacity Planning and Optimization. Better planning and sequencing w.r.t all your constraints will produce better and more realistic results. With finite capacity planning, ICRON ensures that you know any bottle neck and lateness due to these bottlenecks so you can take action.

Thursday, March 31, 2016

Tableau has announced the acquisition of HyPer, in-memory, high performance database system designed for simultaneous, high performance OLTP and OLAP processing.

Hyper high-performance database system will be integrated into Tableau’s product offerings and bring a host of new capabilities to Tableau customers such as faster analysis of large data sizes, richer analytics, enhanced data integration and data transformation as well as support for semi-structured and unstructured data.

Tableau is highly optimized and works quite fast in many applications but in applications where you need to read large volumes of data, like hundreds of millions or billions of rows, it can get dramatically slow (yes there are some illustrations of Tableau with large datasets but they are very isolated, single report demos and in a real life dashboard things can get pretty slow if you need to reaf more than 100 million rows). There are some other tools also available in the market with native Tableau data connections. Most notable and famous of these is EXASOL in-memory analytic database management software.

Like Tableau, HyPer grew out of a research project. started in 2010 by professors Dr. Thomas Neumann and Dr. Alfons Kemper, chair of at the Technical University of Munich (TUM) Database Group. Four of the project’s Ph.D. students, Tobias Muehlbauer, Wolf Roediger, Viktor Leis and Jan Finis, will join the Tableau family, focused on integrating Hyper into Tableau products.[1]

Here is a detailed definition of Tableau's new HyPer in the project website :

"HyPer is a main-memory-based relational DBMS for mixed OLTP and OLAP workloads. It is a so-called all-in-one New-SQL database system that entirely deviates from classical disk-based DBMS architectures by introducing many innovative ideas including machine code generation for data-centric query processing and multi-version concurrency control, leading to exceptional performance. HyPer’s OLTP throughput is comparable or superior to dedicated transaction processing systems and its OLAP performance matches the best query processing engines — however, HyPer achieves this OLTP and OLAP performance simultaneously on the same database state. Current research focuses on extending HyPer’s functionality beyond OLTP and OLAP processing to exploratory workflows that are deeply integrated into the database kernel by utilizing HyPer’s pioneering compilation infrastructure."[2]